Projects per year
Abstract
RNA recognition motifs (RRM) are the most prevalent class of RNA binding domains in eucaryotes. Their RNA binding preferences have been investigated for almost two decades, and even though some RRM domains are now very well described, their RNA recognition code has remained elusive. An increasing number of experimental structures of RRM-RNA complexes has become available in recent years. Here, we perform an in-depth computational analysis to derive an RNA recognition code for canonical RRMs. We present and validate a computational scoring method to estimate the binding between an RRM and a single stranded RNA, based on structural data from a carefully curated multiple sequence alignment, which can predict RRM binding RNA sequence motifs based on the RRM protein sequence. Given the importance and prevalence of RRMs in humans and other species, this tool could help design RNA binding motifs with uses in medical or synthetic biology applications, leading towards the de novo design of RRMs with specific RNA recognition.
Original language | English |
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Article number | e1010859 |
Number of pages | 24 |
Journal | PLoS Computational Biology |
Volume | 19 |
Issue number | 1 |
DOIs | |
Publication status | Published - 23 Jan 2023 |
Bibliographical note
Publisher Copyright:© 2023 Roca-Martinez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.
Keywords
- RRM
- RNA binding
- Protein alignment
- Protein structures
- prediction
Fingerprint
Dive into the research topics of 'Deciphering the RRM-RNA recognition code: A computational analysis'. Together they form a unique fingerprint.Projects
- 1 Finished
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EU587: RNAct: Enabling proteins with RNA recognition motifs for synthetic biology and bio-analytics.
1/01/19 → 31/12/22
Project: Fundamental
Datasets
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RRMScorer datasets
Roca Martinez, J. (Creator), Vranken, W. (Supervisor) & Dhondge, H. B. (Data Collector), PLOS Computational Biology, 28 Jun 2022
DOI: 10.1371/journal.pcbi.1010859, https://bitbucket.org/bio2byte/rrmscorer/src/master/Supplementary_information/
Dataset